A number of computational approaches to model key abilities for collaborating with others have been proposed. Examples include joint attention [1]},
goal recognition [2]}, [3]},
online planning [4]},
or collaborative discourse [5]}.
Most importantly for our work, significant advancements have been made in developing computational models of interpreting an action of another agent in terms of the intentions, beliefs, or emotional states that may have caused said action.
This so-called mind-reading, or Theory of Mind (ToM), requires the observer to have a good model of the acting agent. Commonly one assumes a naive rational agent that selects actions which maximize a subjective utility [6]}, [7]}.
Earlier work defined such models in terms of the BDI framework [8]}, while later work shifted to probabilistic models [9]} or Bayesian models based on inverse planning [6]}, [11]}, [12]}.
In the context of ToM, these inverse planning models have also been termed Bayesian Theory of Mind (BToM) [13]}. Such models have been able to fit empirical data and thus have great conceptual and theoretical merits, as they offer computational accounts of how humans reason and act in the social domain.
However, the standard BToM framework requires a complete generative model for all actions, which is costly to acquire, formulate, and compute [14]}. Full BToM is thus too complex for on-the-fly coordination in real-time interaction (see Sect.  for runtime measurements) and can only be applied in approximated schemes [15]}. Recently, work has also started to explore the use of deep learning to recognize different kinds of synthetic agents and to predict their future behavior [16]}.
